{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#导入包\n",
    "from sklearn import neighbors\n",
    "from sklearn import datasets\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn import tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#导入鸢尾花数据\n",
    "iris = datasets.load_iris()\n",
    "x_data = iris.data[:,:2]\n",
    "y_data = iris.target\n",
    "x_train,x_test,y_train,y_test = train_test_split(x_data,y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = neighbors.KNeighborsClassifier()\n",
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot(model):\n",
    "    #获取数据值所在的范围\n",
    "    x_min,x_max = x_data[:,0].min()-1,x_data[:,0].max()+1\n",
    "    y_min,y_max = x_data[:,1].min()-1,x_data[:,1].max()+1\n",
    "    \n",
    "    #生成网格矩阵\n",
    "    xx,yy = np.meshgrid(np.arange(x_min,x_max,0.02),\n",
    "                       np.arange(y_min,y_max,0.02))\n",
    "    \n",
    "    z = model.predict(np.c_[xx.ravel(),yy.ravel()])\n",
    "    z = z.reshape(xx.shape)\n",
    "    \n",
    "    #等高线图\n",
    "    cs = plt.contourf(xx,yy,z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.maximum.reduce will be axis=0, not the current None, to match np.maximum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n",
      "D:\\Anaconda\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.minimum.reduce will be axis=0, not the current None, to match np.minimum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n"
     ]
    },
    {
     "data": {
      "image/png": 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eoTeBucKE9FrHL1pXfW7znlbRnHb1QBxRDowxDLh6EHtiPO+1sYfqV9sfudrO1L7aHO9b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\npZQKQRqclVIqBP0/ZAPniqXcIaUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a5258237f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.81578947368421051"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#画图\n",
    "plot(knn)\n",
    "#画散点图\n",
    "plt.scatter(x_data[:,0],x_data[:,1],c=y_data)\n",
    "plt.show()\n",
    "\n",
    "#准确率\n",
    "knn.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree = tree.DecisionTreeClassifier()\n",
    "dtree.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.maximum.reduce will be axis=0, not the current None, to match np.maximum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n",
      "D:\\Anaconda\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.minimum.reduce will be axis=0, not the current None, to match np.minimum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n"
     ]
    },
    {
     "data": {
      "image/png": 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tCe0acvDHFZw14SQi6klj5UnjTmDhz1tomlN4ZNhPd6Rxa0m4ixH3DsYV7qJe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a523d61128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.78947368421052633"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#画图\n",
    "plot(dtree)\n",
    "#画散点图\n",
    "plt.scatter(x_data[:,0],x_data[:,1],c=y_data)\n",
    "plt.show()\n",
    "\n",
    "#准确率\n",
    "dtree.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.maximum.reduce will be axis=0, not the current None, to match np.maximum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n",
      "D:\\Anaconda\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.minimum.reduce will be axis=0, not the current None, to match np.minimum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n"
     ]
    },
    {
     "data": {
      "image/png": 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pcK7Vnciv70ykf/Zv4AHK88IhzE/V5bIWrWfDNU/zq6+vomXXJDIeXsxP3xyl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a528138128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.81578947368421051"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagging_knn = BaggingClassifier(knn,n_estimators=100)\n",
    "#输入数据建立模型\n",
    "bagging_knn.fit(x_train,y_train)\n",
    "plot(bagging_knn)\n",
    "#样本散点图\n",
    "plt.scatter(x_data[:,0],x_data[:,1],c=y_data)\n",
    "plt.show()\n",
    "bagging_knn.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.maximum.reduce will be axis=0, not the current None, to match np.maximum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n",
      "D:\\Anaconda\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.minimum.reduce will be axis=0, not the current None, to match np.minimum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n"
     ]
    },
    {
     "data": {
      "image/png": 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OzBWjWeGBTTth2UiZuToXmBJnaJllaW5ho4E36xgGHbcVKtduJI/dAzkrM5DMf7CHF6ma\nSYOzY0VT5ixvJyQiA0VOZzcSwO9FTnsfpPHwTSS4F87dUQ94HxlCXtf744923p/QMMYQd0bTMrcb\n3ms6AGN7T6Il3wAyi4nNttxgpb4/xMLzxy1bgbYVKNObyCfyK+/jtt7nlPKH5pxrjTxkcElhjbJw\nqalcTh1cEkPRQJXaIRuIMubElyEc+ZSyKrjf4p9sNJLrVsofWnOuNVohvTm+BbohvTfCgcuRqXKi\nkNAxB1nxpGYay6QSz10IvI1ljoEOFpa4IDoKOmWXfH0grgAeQT75hkhmvqLTV6naQ4NzlclE+gr/\nhDTe3YsMLvHlCDAeSUPUQ1azHoL0f/4eCaLNkD7O/uZ3w5GBLY8io/mivOUZiOSaP0E6mN1HyT7S\nzpG17QBrbpzI0UVbiGudSNe376bBOd38eu1YJpFovinxfCLwihvGuwyLsDQwhjezLTnA7S7DLo/F\nhXxSzyErjDyPnKWuwB/x3bOjP/KJ/wMZLN8a6UF+1PualUjS6PdIE+6nyGUyFznbjwLbdsOiex47\n5XhLswWYgLQsNEf+gjr69ckoJzLWVqzR48SOjLHl2ZcxtWW1iyeQr/NFyNfzP8hX3VcvjDuRG+Kh\nSJe3j5FeETORyYTikcEfecA0P98/B2k8HIg0Ua1DaskfcfrGvuApOZFRYKzHw4+9xtFnTHsG3t+P\nbd9v59PfzGTwipeJbn66RlMxc+Vo5pc2ut2HW1wy1PtKj6Q43jOQYmV4zuVIY+I84CASVP11D3J5\nLjy7HyH9Zd4FrkXO7ldID5KNHSNo9pvkU453c3IG2z4/dZ+5wDXAIIrObirwAVV1dmuv5ArEUGMM\n1lqfK4lpzrnKLEFqpNFIrbeH97ni3MAqpIEvCqlvdQJ+QAJrYyRwD0N6WfhrJ1J77ovknnshg1K2\nBH4oIZJ3IJ3cPUc4+9FBRMRG0GFEe5IGJJG+eFPZLy6HXRaGeSSQNgSSLSw0RU2fkUAKMj4yx899\nupEe7Cef3c7I/VBvis7uUGS2lD0HKXG8i4+U3O8OZM7Bk89uPNKVT1VPGpyrTALSLQ0kjXAY3ykJ\nFxIODnofe5DhEfFIPavQQeTr6K+6SJ/pwmauHGTODH97Y4ReeN1Y3DkFHNspA14Kcgs4uukoEYnx\nlfJ+EcacOAsA+10QZ+XMFc7XdxRpPPR36d3Szm4DTh2ReBA5M/k57hLHm+jjzar/2VXFac65yjyA\nrHTdFfnaePCdczbIMImXkTrVAeSr+wRwAzJTXALSH/le72u2I0tZNUNGAuJ93atI6uN2pFve5ciE\n+e2QOtWFVKcmqrCYKDo8O4Z3h7xPp5Ed2PXTHuJ6t6f+2V0q5f3u8lheAJYb+RQPeST18DfkLDRF\nmlXvp/RaTh6SZz6IzJo9BFm24FVkmE/h2f0dMkDmNYomcX0ByLnHMqHY8Z7NQX4s9j6NkTVnTj67\nw5Cxpqp60uBcZQYh0+4sQbKAwyh9Wp26SLZzB1IXOgMZCPKhdx+ZSJPUIKSX7ktIhnIPcsM8ArgN\n+dpHAjcjzUP3IDPTbUEmJhoc1COsCm1+dykJfc8gffEmkoYOpcnlAzDGZ8quwrojtVxji+bra4J8\n8qlIjfc3FF0Ok6879fXZ+dDoc0NEgSXRZXjSbRmFBOLWSINgfaSXebp3f3WQtMZhZJj536+3fHTp\ng6ce7w1X+SzvnUhv9a3IZXhQED4DFTraIOhII4BLkYCbD0xBhnMXH5adhzQw3ozUnXKQYQ4W+fqP\nRGric5AVT76t9JKfTiANgjPHjK7w+w2fOv3UfQbYIDgOyTUPoKg/y0CkSba45OtKvt+ii/5IxLer\nuM0jteH1wHQDs318TW5CztT1SC18KXK2slYUDZw5cRxjRjPf33ZgVekqq0FQa86O40Z6dRTekEYg\nteYDPrY9hnztG3sfRyM32zuQwF54zlsDCyqnuJVgLJOCEnxmPjK6RGALxH6Kxk0apHfGvtI3LyF7\n+yHaUzTspzVQUMr3OM37Xq6TttUBK7VbmQ2CxpgoY8xCY8xyY8xqY8z4qihY7RWGzK2xgKKGww1I\nhrK4+siN9wrv4z1Ir4yGSG/cHCQ9Uti3WgWiO3K/4UaSS6uRPjb+anhBL1Z7JPBaYL6BqDDfKZhO\nSMIrAwnKP+J/I6OqmcqsOVtrc40x51prs4wxYcB8Y8x/rbWLqqB81dQBZCL7g0gHqRuRoPsp0iUu\nHriF0mdu+DPSKDgP+aqOw3dwDkPmT7sPyT17kMzjaCQ18rx3uyikP3QW0iN3M1KzvoPy9oI9OUWR\ns/swmyd8QN7eIyQkd6XtQ6NwhZeceAjg0KwVrL//LTzZedQ5qyO9po3DGMO2iTNIS11NRON6HBjv\nLnWqpwzgEQO7XIYEC097LG2R/O0HSCAdgbcP8SFY0Pf35G/dT0TLRux6IbBjvB9phn0e+WQvR5Yk\n+AmZecSNXB7fAayFnZO+5tBXiwmvG0fbx69i++H/0hx4HTlTBRaecVvc3rIuQQbAFC4Udg3SDGyQ\n+6XXT1M2X8ebhTRYbkZq3rcRnD7Ohf24DyK98q/3Hs9nyF9oPJJ7bxOE91JFAso5G2Nikehyp7V2\ncbH/05wzIGv/3YCMzWqOfAU7e3+fgeSNjyK9WCdTtOp1cRZJW8Rx+mvoKKQJaQDS0LcKmd5zAjJG\nzSAZzHHIxcGDBPoNFOWoA89uFQbn/LRMfurzAD2v6UjLgUkseHUptGtLt7dKjmRLX7KJRUP/wNmP\nD6ZR14akPvkDnoaJJJzZlrylaxn84FnsW3GAn99Zwj93ZfvsaHilyxCDZYAHtrhglYWnrQTLFCSo\nfY9crv5Wx9A0C3p6LGtdhm2Rlk9yAl/dJMO73yhkLr/bkDVpWgHzkca8h3ob/upuQsr4wRzdksai\nF37k8QM5PAUMMdDIwmwjSaeOFpYhZ2c/UiN/D+lYeRj56yic8irZR875yQtHc9GsU4/3fiRYWorO\nbjYyOrEiucvjSOA9+a+58PevOPWv+W38X5u9JglpztkYU9hGcQbwevHArE62EKlPFc4S1xbpFFUP\nWVevMD98DGnyuamU/RjK7qW6G6nPPIp8TTsjaY03kJ4Yhe31DZGZ5g4iPTZcyFfsDSTcdPb34Eo4\n9M0KGnepz7A/pwDQ5rzWvNjwr3R5/XZckaf2w9783Kd0uaIjQ55IBqBp7yb8vcsk0hZu5ne77iY2\nMZbOozpx5Od9/PjxRob7OlqPLTpaD+x0wVtWjrZwQaxI4N9ATqblSu9Mcx09lldypWZxYYDHWOek\n399AAlPhPtoitd5Xtxiu/PFSGnVtBMCxbWm8+foyurgM53jky9vUSm34F+RSGYsE0sNIGmM4UpMu\na6zjpA0lj3cq0tuj+NndTNGaOuWxCP//mr9DqiUqOPwKztZaD9DbGJMAfGaM6WqtXVt8uwkTJpz4\nPSUlhZSUlCAVsyYITq+Y4L5X5XRBK1XxGoYxWAu+esJV9NOqyk/b3zc83acdSHmdcHZ9laHKP/Nq\nKDU1ldTUVL+2DbgrnTHmSSDTWvtysec1rQGUntZIAr7E/7SGv0YhdbD+SA/XlRSlNVKQetVspHft\np97XdCbYaY1e13akxcAkFry6DNq2KTut0a0hqX/4AU9iIvFntiVv6Rr6je3NofWHWTdlGe/tyva5\n0NVolyEOS38PbHXBymJpjcKjvRf4W5whKRt6+EhrZCHB6+QpPd1ICiOBUwNbpvcTOjmt0R/pT1OY\n1vh9bxevuhtLWmNzGoteLJnW+N5ITbOjheVIImofktaYgu+FvcpKaxQe731IWsNQdHZPTmv4Ol5/\nlJbWSEJmejn5r/ktKv7XXB1VVlqjzOBsjGkI5Ftr040xMcDXwHPW2pnFttPgfMLJDYJnIqmLNORr\nnYaEgSuQgFlRWcjow51IWHkWGSO2HGkEdCN9plO8277JqQ2CdYrv0C8+GwT3HJYGwYcvL7VBcM1d\nk9j/fiqucBceVxiDlryAzfew5LwnyTt0HOuB659uy+2PbCz1aB8q1iDYGngswsWSfA/GQJsIF6/l\neZj9eS9euHoN4W4PbpeLe6d0ZcSY1TwTE868fOmodn644dEcN4uRmeLc3k/kz0jwfSI2glX5bjwe\ny+gwF/fkuVmANMMWUNQg2HMkdPi5MQWHj+Ep8JB024Vs3fslE6fBSy5DjoFubsvTSJCchuQJCxsE\nS1smwFdwnjlmNK9Pk6mzijcIvkPR2b0VCd7PIA13IEOfHiGwy/HJDYK9kKpHYYPgXIoaBCuyMEF1\nFsrg3ANpr3B5f/5jrX3Wx3YanE9rHPLxnY/Uz/4FPE51HcdVnhnmDs/5mbU3vczNP1xP3VYJpI6f\ny/ofj1JwPJveV7Zh8O8HcGTTUf499J+8vDfLZybcV7Da/tpM0j6YxQ0zryI8Opzp188gp0lL9n/8\nE6PevZgOw9uzdfY2Pr72C5rdlIJr/Qau+nAk1m2ZNnI656Ru5XOP5FBbIiPzZgP9osPYN7IjI/49\nktxjuUwb/B6/Xn+kRC4c4Jl2EaRd0ZcL/nIux/dmMGXoVDq8dieNLj51sd1AB5CUFpz93cckpDvg\naKQp+CPkL/BG/4ugyhCyBkFr7WqK+uKrcvsZqTm7kBpuF+9z1TM4l0f6wo10vbIT9VpLQ+egB/vz\n00uv4cnJZ9D8qzHGkNihAR1GnMEvb6/2u5ny2IJfOOu2nkQlyJKsA+7tw4xxc4ltFEuH4dLvoe15\nbajbtj7p89Zy0YR+RMRIY2W/sWey9KedNM0uODHspxvSuLUq3MWIRwfjCncR0yCGHvecxeqHZvPs\n5QUlyvDjty5++0B/jMuQ0Dye7td2Yv/CDSWC8xdTL+QySs4pXZqKDKIB6bvTl6IpsnpTtK67cjYd\nIVhlGiEj97ohdZg9+F4lu+aKbpnIrs/34s53ExYRxs55u4ht2YCC4zns/HEXrc9pRUFuAXsW76VR\nAPuNatmI7fO20/OmHhhj2DFvF1EtG3JkzlrSdx6jbssEMvZlkLb5CInndWfHvF20v/gMALbP20VS\ngYclSFogFuk9kQW0trBzzg6antkEay27v9vGwNwCvph6IW9wxyllCO/7ADvn7aTrVV3wuD3s/Gkv\nCWN6lSjrG9zBG1PvKPF8ZWmKJLw6eB/vpHZ2d6uOdG6NKrMGyQ23RPLOScBEqsX1cc6AEk9dcs4n\nAe9mxrLRjLgyjEVZkcTUj+HYjnS+HOTm9w8+wcprXyShRTzZh7PplZLA8//ZSzaSXshBGuAK588r\nnGCoMEjmH81gccofqFM3jIi4CA6sPUy/1D9xcMZiNv5hKpF1IsjNyKfto5cze9EH9J8ThiexDtZt\niTmWyWtH3HwAfI7kT9ORLmk9gbGRLkxCFO58D41zC3gzx01GwQU8/XEPcvYepd7AjtQf2JG0hRtY\nfumzNB/cgmPb0zFNGtF7xhO4Inyf3/Qlmzgy7xeiGtel6VWDSt3Ol0DSGgeQYUl1kfx0LtJIWA/J\nQ+9BAnd5bo23IHnzOsjkTVHl2EdNELKccwBvosG5TAeRG806yOxwvhvNHGXOgHIF4uLGMok65hvu\nRYJtIrJaxxPA9qth8tdRdLumC3uX7iNrbzrv7cnmPmQITh2kb/BzSINUoZMnG3Jn53Lo21XYAg+J\nKd2IqF+Hhec8Tv7WPXS8tAOb/ruZNvGZPLK6gDuRBjMP0nd6EtKQthK5dG5C5uxLBF5E7nUykfue\nd4CJlzZhz6FIkvo2Zd0n62n39PW0/O0wcvYc4ej8XwivG0vD83tgwnyf3z1Tf2Djg+/S5crO7Ft5\ngPzoeHrPfKrURtTiAs1bZyJB1CB/ddFIs/Fq7/FuRHLSpfW492Uh0h+oC0XD0/9B4AN8agKd+KhG\naERRd/4qdu4A+TYFKBiBudC3yBx7NyKBojty73DkizBuXnADTXs1weP28PZZkxm/J5sGSEdBkJ4A\nryLB0ZewmCiaXNrvxOOjizdxfNkW7tsug1tyj+Xy11av8TwF9AeSvdt9793vz8hSUZFI97HXvb//\niqILwufIjNw5uzzcsuh6XOEuBtzbhzf7vkuLm88jOqkBza46/TSs1lp+ue9tbvru2hPH++45Uznw\n+SKajq5lP2XIAAAWDElEQVSc9oc44JyTHq9Hur4VHm8ycryXI3cO/ngFmfOwPRKYP0K61l0RnCIr\nNDjXHhOCG2jLIx0Zq1hYTWiMjCxz53lo1FWWSHWFuWjcvRHpK/ZzxkmvbeR9vb9yth4gJjGG2ESZ\n8CkqIYr4ZnGkpedy8nKwjYC1SC25cKKheKQ/cLb3/ws1QdIATTo3wBUuc4Y16NAAm+/Gk5NPWKwf\nN/YeD3np2accb6MuieQdOh7A0VVMOr6P9zj+B+djFI0ONN79pQWxjEqXqVJVqDdSQ92N5D5nI7nO\nBo0j+Pbh2eRl5rFj3k7Wf7aeFGSuvQNIkJyDzGfhr8RhPcg+ksOyt1eQn5XP6vfXcGznMc5Ghkof\nR4LUAqRWeQBJs+Qht+wxyC3/d0jj4CHv6/oDW2ZtY+vsbeRn5fP9Uz9Qt08b/wIzYMLCaDi0C989\nNufE8W74YiP1zy7/EPpAdcD38TY+3YuK6YvcdeQgK1mupmg4uQoOzTlXQ3bFwIAmjS902JbsZVCa\n7O0HWP/Au2Rv2Ud873Z0eukWIuqXb8BKoSfMaKYgPbzzgeYGXrNwxnTof39dsvceIzwmgpH3Ned3\n/7eVGyhawtYga5CfPBnSyTnnfdN/YufEz/Hku2n26/NoeefF7P9iEet++xq5R7OJrBvF5IdzaP6Y\nLAX1pXcfo5GhOHOB/0Pys/WQmeiaIwMu0iiar+JNYNb/+vDi2B1k70kjMbkD3f/9ANFJDfz+HHIP\npLPmpokc+n4d0U0S6Pza7TS5zP9LTzAm21+DDKTZi0yY80cCW9Iq0/v6BUgPl7vBZ//v2kAbBNUJ\n9rqB5fpy+hrQ4EtBRjY/nfkAfX/ThfYXt2PZO6vYseY4/eY8i3GV/2Yrc9Nelg16kPNevICGnRP5\n4alUzmhvcb0+8ZTtxjKJd8w3/A/Ja8YjM6BlA+kr4IteMu1Q4YXm4NfLWXfLq1z65sVExEXw1V2z\naHb/KFrdcdEp+y1tJZQspDGsKxKoViLBZwgyj+CF3veeiYwcbBHARa4y6EoozlJZwVnTGqqE9EWb\niG8SzTl/SCbprGaMeP1CsjbuJWf3kQrt98CMJXS4qju9ft2T5gOac+l7I/l+6kGf236LDM/phnSh\nuxxJhYC3r/BJwXH/B3MZ+uRgOoxoT5uU1lz8ynkcmDbH73KtQ27rz0Fqyxcj/YFnen9vjcxXMYBQ\nL/SlahMNzqoEV3QEuem5eNwy/0R+dj7unHxcURFlvLLs/eYczT3xOPtoTqn7NEht9cS2lD7Lmis6\nguyjOUXbHsnGBFDWSCR3WrgsVAGSdoksVoYcam9fXlX1tLeGKqFu/w64GjXgo6s/p/0FrVk1dR1N\nrhhIVOOy5pc+lScvn8xN+whPiCWmRSLNrk5mwQufMvPeb2jcJZGfXllK20el85U7O5eszfuJbBgP\nTeEuioboJCANgqWNbGt59wh+PPdJ3HluIutEMv/5hXT71zgA8tMzydlxiOiWDUstZxekV8Z0pP/z\nWmQioSFI7vkwEqRXIblVf41lEpetLDlU+3SppeKvqejw7cqUD+xCuuoF0pio/KPBWZXgCg+jz8wn\n2fbKl6xZspeEa4bRauxFZb/wJFlb97Ps4qdx2QJyDmeRdONQOk38LQN++gvbXpnBwRXHaPvn39B0\n9CCOrdrG8l89S1RcOJn7jrPr9yNZdy388L2LRQc8hBlDWIRh7sWeE/nmk8V3b0W/OX9ix6SvsQfc\n9PrkMeond2b/F4v5+bqJxIa5yCxw8+EESVsUF45cCP6DpDNGeX/CkAEw3yIXiDeQRV4P+3H8Y5lE\novmG+T7+b+aK0heeLf6a020bSnuQORULkPz8xd7HVTxDeI2mwVn5FBYTxRmPjS7369f+9m/0u7Ur\nyQ8NJCc9h8lDp7H/kwU0HT2ITn8+dU601de9xLBnk+l1Yw8y9mfw9sB/0eHay8luu4QHN19DRGwE\nqePnctaqbHqX0hAX37UlXf5664nH+WmZ/DxmIjdm5Z5YNfu2p2SFFF/9KqLwPUKuL8HvIjb/TAm6\nPv8vyO9VWZ5F2gOSkXTPP5GukikhLFNNo8FZVYrja3bR8/0LAIiuG03nS9uxb/X2EqPgrNvN8V/2\n0mOMDA2p06QO7S5oy+6lm+h1eXsi42SoRM8burHiEv9rkNnbDlAnzEUL7+OmQExULA2GZJHsx6xK\nld0bojxdIZ1kKzI3NMiQ7fbIPNIpoSpQDaTBWVWKuA5NWf/5Bs4a24f87Hw2fbOdJveX7MtrwsKI\na9uIDTM20nlUJ3LSctg+Zwd1LxnAhv/+TP/7+xEeFc76LzYR19H/dTaiWzbkeF4B+5GRfYeBtPwC\nnnzvLb/6JM98xHe3OyWaI/Od9ENyz9vQwBxsGpxrqL1IV7ACZDaP9qfZ9vjaneybNhdrDM1vSiGu\nfcUnlezy1t18f/5TLPnHcjL3Z1Lv3J40uzaZ/LRMdkz6moIjGSRe0IuGw3rR/d/j+OySp4m682ty\nMvJpcct5dHrx16y+7kVe6/QWMQ1jyTyax1mzJvj9/pGJ8XR5+07eve0NGkSGcySvgI5/vaXUwFyQ\nlcPq3/6dnA17SBjYCc+tPjcL2HFkTo50ZHRhIKMcnexxZPmINchQ7n4U1aRVcGhwroF2IaPeuiKT\nrN8L/IWiyX5Olr5kE0sveYa+t/bA47YsGvwYZ33/NPHdWvnY2n/HV27DhaV5/6ZkHcph9+KNZO84\nxPIRz9CyTyJJnRuw5JZXaTP+OtxpmcTkuemXls3uqHD2fb0K+1wBPT98mOOrt+POzCW+Z2vC4wKb\n8yzp+qE0OK8nWRv30KldU2Ja+F7X2lNQwI+t7iDxaCbdPJaVK7bS8IeWHFmx0+f2w/0cgJIJ3I7M\nO9EAeNr7+NKAjsKZ2iIrfm9CGk3boo2BwabBuQb6gFMbZxoA7yLz+ha37bmPOf+Zs+k7Vmb0jWsU\ny4YXP6P75PsqVIYt46dx9fTLaTlYsr6f3DCD9Q//kyadErjiXxKeOoxoz7+GT8V9NIs7c/JpANjc\nAqbsPsz+zxaRdN0QEnq2qVA5opvVJ7qZr6VTi+x+L5WwIxn82koPjbM88NKanZwT/Sp1Ovnq3+Gf\nwl4ehTPrdUCGf9eE4AwybLtnqAtRg+kglBook1NnF0tAhij74s7IIb550dbxzeNxZ2SXsrX/Cort\nN6F5HQqOZ5Nw8nMt4inIyMGd7z5RXgMkeCzujByqSv6RDGJd5sTs2tFAuIH8Q8cqtN9sSp6Hqjsq\nVd1pcK6BUpAZ1HYgXchmIytV+NLo8kF8+/gP7F22j90Ld5M6YT6NLq/4vMKNR/Xnq7u+4dAvh9g4\ncxPLJ6+mxS3n8fMH61j/+QYObzjMjDu+ptkVA2gyrCefR0VwGMlhbjCQeH6PCpfBX82uSeawhYVG\nGg5nuYDIcOoO6FDWS0+rPzIL3y/IrHYzkYEtSvlD0xrV0SPAabp6DQVSgWkGrIXOLrjSg88BHC1u\nv5CCjBw+HDMTXIYWvxtF0phzSmwXqHZPXs2Cvg/yTv8pGJeh3gV9aHZVMpGJCcx6ZAr5RzJIvPBM\nurx0C9bt4Zfb/s7k2WuIalyX3m+OJbZd0wqXwV8xrRrRc8ZjzLn2Zb7LzCWyYTx9Z43HFV6xr0c7\nZOa2vyENg/2B+yteXFVL6Kx01dAldvdp/3/vh/PZ+sR7XPXhSCLjIvj01zNJGHUO7R6punUq5ve4\nj8atYxj+94s4uiWND0d+TIdXbqXlzaXV4WuWwhGCFeVrJkGdlc5ZdFY65bfDXy1myKMDaNa7KYkd\nExn2f0M4/OXiKi1D9tYDDP/HxdRtVZc2Ka3pO7Y3e/7t/0xxSvi621G1g6Y1aqCwhFiObi1a1Ono\nljTC68ZWaRlc4WGkbU2jbssEAA5vPEpEvXplvKp28HdebVW7aXAOOYssuZkOdOLUtT7Kp/WDo1g0\n+FEyD2YRVSeSFf/8mT5f/QGQCYkyN+whtn0z4s6ovLxu0m0X8p9LP6LvnX04vOEIW2dvZ/CqcZX2\nfsGQsX63DPvu0oKYVn6M8VaqEmlwDikPskDQUiQoH0TmR+taob3GtmnMwMUvsOf9uWTmF9B/7hjq\ndG7BrsnfsfHh92jUqykHV+3njAnX0uquSyp8FL4k3TiUXW/NYsmk5Xjy3DQ4txvRzf1fyqmqbX3h\nU7a/+BkNezRhzcp9dH79Dppd7WvYjn/e4A5mrvhGRv8Uo6kK5Q8NziGViswefAcylm8NMo7sgwrv\nObp5Iu0eHnXicd6hY6x/YDK3LryJxI6JpG1L482+U2h0Wf9SR85VxLrb/85FE8+j9829KMgt4L3z\nP2DP1Lk0vzEl6O9VURnrd7P9xc+4Y/nNxCfFs3/VAaYM/QeNRvQNeFTiyYb3mi7D6JQqB20QDKnd\nyLKahat2tKdoSdPgytl1mDot6pLYUQJxvTb1qNc+kZztvpeJqqisLQdof/EZAIRHhdPu3JZkbd5X\nKe9VUdnbDtCwRxPik2TISJOejYmqG03uvrQQl0zVZhqcQ6ojsBHI8D5exumnKCq/mLaNydyXwY65\nOwDYvWgPaZuPENux4pMc+ZLQuw3L3l6BtZasw1ms+3QTCb3bVcp7VVSdLi04sGIf+1cdAGDz11vI\nz3ETXQl3FEr5S9MaITUAWV/6NWSJ0Tjgr5XyThF14+jx/jg+uGIikXER5B7LpfuUe4lqFNjSU/7q\n+vY9LB/xDEsnrSTvWA6t7rmExpdV3pxsu6fNZftT07AFHpLuuYS2D44sddsjc9eyd8p3YAzNb7+I\nev070OX125l8zhtE14smP8dNr48eIqyCayaW1h/5cIhX71bVgw5CcYRjSO25Mf5cL8sahHI67uxc\ncvYcJbpZfcJiK3e5Uut2k73zMOEJMUQ2iC/7BeW0e9pc1o15haHIoqzfAS3HX02HCdeU2Pbw7NWs\nvu5FzvnDIKzb8sOzCzjzi8epP6gTBZk55O5LI7pFYoUD8+kGoVS0K50OQnGWyhqEojVnR0jw/lS+\nsJioSu1CdzITFkZsm8pf+nP7+A8YCpztfRwHzJz4hc/gvPOvX3DBC+fS6yaZu8MV4WLN619Rf1An\nwuOiCa+iz0apsmjOWVV7Nt9N5EmPIwE8vmsznvwCIuOKasWRcZHYAnellk+p8tCas6r2ku4dzncP\nvkcc0u9lhoF6V/qeWa/Zb4bx9e8nExYZhqfAw3dP/EDnN++p0vIq5Q8Nzqraa/vAZRQcz2bmyzPA\n46H+Vcn0ePdun9s2uzoZ6/bw/SvfgIGOr42l8Yhgr6+tVMVpcFY1Qofx19BhfMkcsy9J1w0h6Tqd\nWVk5m+aclVLKgcoMzsaYFsaY2caYn40xq40xFVtcTimlVJn8SWsUAA9Ya1cYY+oAS40x31hrf6nk\nsqlKkLZwAxlrdxHXKYn6gzuHujhKqVKUWXO21u6z1q7w/p4BrAPKvySxCpmtf/mE1Vf9Bc/3c/n5\n+hfZNF5HMijlVAE1CBpj2gBnAjqsr5rJ2XuULc99wl1rbyO+WR2yDmXxepe3SPrNecS2bRLq4iml\nivE7OHtTGh8D93tr0CVMmDDhxO8pKSmkpKRUsHgqWPIOpFOneQLxzeoAENswlrpt65O796gGZ6Wq\nSGpqKqmpqX5t61dwNsaEI4H5X9baz0vb7uTgrJwltn1Tso/ksPbjdXQZ3ZlNMzeTvuMYdbq2DHXR\nlKo1ilda//jHP5a6rb8153eBtdbaypkyTVW68Lhozvz8cf53zQt8OuYLopvW5czpjxBRLy7URVNK\n+VBmcDbGJAPXA6uNMcuRRe8et9b+r7ILp4KrXr/2nLNlEu7sXMJiKndGOqVUxZQZnK2184GwKiiL\nqiIamJVyPh0hqJRSDqTBWSmlHEiDs1JKOZAGZ6WUciANzkop5UAanJVSyoE0OCullANpcFZKKQfS\n4KyUUg6kwVkppRxIg7NSSjmQBmellHIgDc5KKeVAGpyVUsqBNDgrpZQDaXBWSikH0uCslFIOpMFZ\nKaUcSIOzUko5kAZnpZRyIA3OSinlQBqclVLKgTQ4K6WUA2lwVkopB9LgrJRSDqTBWSmlHEiDs1JK\nOZAGZ6WUciANzkop5UAanJVSyoE0OCullANpcFZKKQfS4KyUUg6kwVkppRxIg7NSSjmQBmellHIg\nDc5KKeVAZQZnY8w7xpj9xphVVVEgpZRS/tWcJwMXVXZBlFJKFSkzOFtr5wFHq6AsSimlvDTnrJRS\nDqTBWSmlHCg8mDubMGHCid9TUlJISUkp8zXWDghmEZRyiCvAlv6/Myuy66mW5KkV2YEKldTUVFJT\nU/3a1lh7mr+gwo2MaQPMsNb2OM021p99KaWUEsYYrLXG1//505VuKvAj0NEYs8MYc3OwC6iUUupU\nftWc/dqR1pyVUiogFao5K6WUqnoanJVSyoE0OJ+Gv62q1VFNPbaaelxQc4+tph4XVOzYNDifhv7R\nVD819big5h5bTT0u0OCslFI1jgZnpZRyoKB2pQvKjpRSqhYprStd0IKzUkqp4NG0hlJKOZAGZ6WU\nciANzqUwxriMMcuMMV+EuizBZIzZZoxZaYxZboxZFOryBJMxpq4x5iNjzDpjzM/GmGo/5aExpqP3\nXC3z/ptujLkv1OUKFmPMOGPMGmPMKmPM+8aYyFCXKRiMMfcbY1Z7f8p1vjTnXApjzDigL5Bgrb0s\n1OUJFmPMFqCvtbbGrW5jjJkCzLHWTjbGhAOx1tpjIS5W0BhjXMAuYIC1dmeoy1NRxpgkYB7Q2Vqb\nZ4z5D/CVtfafIS5ahRhjugHTgH5AAfBfYKy1dksg+9Gasw/GmBbAcODtUJelEhhq4Hk3xiQAQ6y1\nkwGstQU1KTB7DQM214TAfJIwIK7wYgrsCXF5gqELsNBam2utdQM/AFcEupMa9yUNkonAQ5x2uvRq\nywKzjDGLjTG3hbowQdQWOGSMmexNAbxpjIkJdaGC7BqkRlYjWGv3AC8BO4DdQJq19tvQlioo1gBD\njDH1jTGxSEWvZaA70eBcjDFmBLDfWrsCqWX67INYjSVba/sgfzB3G2PODnWBgiQc6AO87j2+LODR\n0BYpeIwxEcBlwEehLkuwGGPqASOB1kASUMcYMya0pao4a+0vwF+AWciiN8sBd6D70eBcUjJwmTc3\nOw041xhTrXNgJ7PW7vX+exD4FOgf2hIFzS5gp7V2iffxx0iwrikuAZZ6z1tNMQzYYq094r39/wQY\nHOIyBYW1drK19ixrbQqQBmwIdB8anIux1j5urW1lrW0HXAvMttbeFOpyBYMxJtYYU8f7exxwIXIL\nVu1Za/cDO40xHb1PnQ+sDWGRgu06alBKw2sHMNAYE22MMcg5WxfiMgWFMaaR999WwOVAwKs+BnWB\nV+V4TYBPvUPtw4H3rbXfhLhMwXQf8L43BbAFqBFLqnnzlsOA20NdlmCy1i4yxnyM3Pbne/99M7Sl\nCprpxpgGyHHdVZ7Gae1Kp5RSDqRpDaWUciANzkop5UAanJVSyoE0OCullANpcFZKKQfS4KyUUg6k\nwVkppRxIg7NSSjnQ/wOXmvQFrnzFAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a52819a7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.84210526315789469"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bagging_dtree = BaggingClassifier(dtree,n_estimators=100)\n",
    "#输入数据建立模型\n",
    "bagging_dtree.fit(x_train,y_train)\n",
    "plot(bagging_dtree)\n",
    "#样本散点图\n",
    "plt.scatter(x_data[:,0],x_data[:,1],c=y_data)\n",
    "plt.show()\n",
    "bagging_dtree.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
